Field of the Invention
Embodiments of the present invention generally relate to configuring a behavioral recognition-based video surveillance system to generate alerts for certain events. More specifically, the embodiments provide techniques allowing a behavioral recognition system to identify events that should always or never result in an alert without impeding the unsupervised learning process of the surveillance system.
Description of the Related Art
Some currently available video surveillance systems provide simple object recognition capabilities. For example, a video surveillance system may be configured to classify a group of pixels (referred to as a “blob”) in a given frame as being a particular object (e.g., a person or vehicle). Once identified, a “blob” may be tracked from frame-to-frame in order to follow the “blob” moving through the scene over time, e.g., a person walking across the field of vision of a video surveillance camera. Further, such systems may be configured to determine when an object has engaged in certain predefined behaviors. For example, the system may include definitions used to recognize the occurrence of a number of predefined events, e.g., the system may evaluate the appearance of an object classified as depicting a car (a vehicle-appear event) coming to a stop over a number of frames (a vehicle-stop event). Thereafter, a new foreground object may appear and be classified as a person (a person-appear event) and the person then walks out of frame (a person-disappear event). Further, the system may be able to recognize the combination of the first two events as a “parking-event.” Such surveillance systems typically require that the objects and/or behaviors which may be recognized by the system be defined in advance. Thus, in practice, these systems rely on predefined definitions for objects and/or behaviors to evaluate a video sequence. More generally, such systems rely on predefined rules and static patterns and are thus often unable to dynamically identify objects, events, behaviors, or patterns, much less even classify them as either normal or anomalous.
On the other hand, a behavioral recognition system is a type of video surveillance system that may be configured to learn, identify, and recognize patterns of behavior by observing a sequence of individual frames, otherwise known as a video stream. Unlike rules-based video surveillance systems, a behavioral recognition system instead learns objects and behavioral patterns by generalizing video input and building memories of what is observed. Over time, a behavioral recognition system uses these memories to distinguish between normal and anomalous behavior captured in the field of view of a video stream. Upon detecting anomalous behavior, the behavioral recognition system publishes an alert to a user notifying the user of the behavior. After several recurrences of a particular event, the behavioral recognition system learns that the event is non-anomalous and ceases publishing subsequent alerts. For example, a behavioral recognition system focused on a building corridor may initially publish alerts each time an individual appears in the corridor at a certain time of day within the field of view of the camera. If this event occurs a sufficient amount of times, the behavioral recognition system may learn that this is non-anomalous behavior and stop alerting a user to this event.
However, although in a plurality of cases this is how a user expects such a system to work, in some instances, the user may want the behavioral recognition system to always publish an alert for a particular behavioral event. Returning to the previous example, if the corridor were of limited access, security personnel may want to be notified each time someone appears in the corridor to ascertain that only people in the corridor are the ones authorized to be there. Conversely, the user may not ever want the behavioral recognition system to publish an alert for a particular behavior. This situation may arise where the event occurs often but infrequently enough to result in an alert. For example, a behavioral recognition system focused on a room in a building that is next to a construction site may create alerts whenever construction vehicles pass through the field of view of the camera outside a window in the room. In this instance, security personnel may not want the behavioral recognition system to ever alert on these occurrences.
Behavioral recognition systems by their very nature avoid the use of predefined rules wherever possible in favor of unsupervised learning. Thus, approaching a solution for these issues requires a natural method for providing feedback to a behavioral recognition system regarding what behaviors should the system either always or never result in an alert.